optimizacion bw
Transcript of optimizacion bw
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Jay NarayananOfficeMax
BW Technical Team Lead
Performance Tuning Massive SAP BW
Systems - Tips & Tricks
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SAP Systems at OfficeMax
Retail and Core Business
Business Intelligence
and ModelingCustomer Call Center
Decentralized
Warehouse Management
Systems
•Order Status
•Inventories•Customers
•Orders
•Campaign Analysis
•Market Basket
(data mining)
•Customer Analysis
•Sales, Inventory
•Purchasing, etc.•Sales Analysis
(infospoke)
WM3
WM1
Las VegasHazelton,
Pennsylvania
McCalla,
Alabama
WM3
WM3WM3
WM2
WM3
Retail
WM3
CRM WM3
BW/SEM
(Cleveland, Dallas)
CA
AZ OK
FL
NM
TX
NV
UT
OR ID
WA
MT ND
SD
IA
MNWI
ILIN OH
MIPA
WVVA
NC
SC
NY
VTNH
ME
MACT
NJ RI
DE
MD
GAALMS
LA
AR
TN
MO KYCO
WY
NE
K S
•Financial Simulation
(transactional cube)
Shaker Heights,
Ohio
Shaker Heights,Ohio
Shaker Heights,
Ohio
•Vendor Shipments
•Deliveries due
•Master Data
•Deliveries made
•Inv Adjustments
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BW Technical Overview
Modeling
Extraction
Changerun
Queries
Compression & Aggregates Rollup
SEM BPS Functions
Agenda
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BW Technology Platform
Solaris 2.8SUN E10K48-CPU / 48GB
Hitachi 9960(6TB/Monthly Growth 25GB)
BW 3.1C (SP15)
SEM 3.2B (sp12)
Oracle 9.2
Windows XP
SAP GUI 6.20
SAP Portal 6.0
Crystal Enterprise 8.5
IGS
1 App Server SUN V8808-CPU / 32GB
NT Servers8-CPU
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Cubes Aggregates ODS
SALES 12 40 5
INVENTORY 12 76
PURCHASING 2 4 2
FINANCE 6 4
HR 1 2
CRM 1 3 2
SEM 6
STAFFWORKS 3 3 3
BW STATISTICS 6
Others 5 4
Application Areas & InfoProviders
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Performance Statistics for Loads
242 hrs1.4 mCube & ODSPurchasing
1040 mins140 KFull/Delta LoadMaster Data(27 info objects)
75
1
2 hours
1.5 hours
27 m
27 m
Cube Load
Rollup
Inventory
Snapshot
Change run
Cube Load
ODS
Rollup
Compression
ProcessProcess
83 hours20000Master Data
Alignment
24
24
8
1
30 mins
1.5 hours (actv)
15 minutes
20 mins
1.8m
1.8 m
1.8 m
Sales
StreamsStreamsRuntimeRuntimeRecordsRecordsDataData
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BW Users - Yearly
0
100
200
300
400
500
600
700
Year-
2004
Year-
2003
Year-
2002
Year
2001
Users
680
504
195
149
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BW Users - Daily
2
226
141
9691
98
57
178
150
121117
99106
52
92
222
0
50
100
150
200
250
8/16/2004 8/23/2004 8/30/2004
Users
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BW Technical Overview
Modeling
Extraction
Changerun
Queries
Compression & Aggregates Rollup
SEM BPS Functions
Agenda
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InfoCube with no Line Item Dimension
Dimension
Table
Master data
Sid table
Fact TableMaster data
Sid tableMaster Data
Sid table
InfoCube with Line Item Dimension
Dimension
Table
Fact Table
Dimension
TableMaster Data
Sid Table
Database
View
Modeling – Performance aspects - 1
• Line Item Dimensions
M d li P f A t 2
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Modeling – Performance Aspects - 2
Line Item Dimensions & Cardinality
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Modeling – Performance Aspects - 3
Logical Partitioning – Yearly cubes Parallel queries run on basic infocubes
Change run can be run parallel
Year 2004 Cube Year 2003 Cube Year 2002 Cube
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Modeling – Performance aspects - 4
Run Program SAP_INFOCUBE_DESIGNS for existing Infocubes
Compression ratio
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Modeling – Performance Aspects - 5
Physical Partitioning
Aggregates
Navigational attributes
Database partitions and
secondary Indexes on ODS.Dimensions with more than one Characteristic
A d
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BW Technical Overview
Modeling
Extraction
Changerun
Queries
Compression & Aggregates Rollup
SEM BPS Functions
Agenda
D t L d P f
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Data Load Performance
Data Load from Source System Configuring ROIDOCPRMS table
Transaction SBIW
General Settings->Maintain Control Parameters for Data Transfer
Refer OSS Note : 417307 Infosource specific values can be maintained in Scheduler
TSV_TNEW_PAGE_ALLOC_FAILED if Packet Size is Huge
Reorg ARFCSDATA & ARFCSSTATE tables
Flat File load Performance
Maintain the parameters thru transaction RSCUSTV6
Load from application server not from Client
reduces network load.
D t L d P f N b R B ff i 1
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Data Load Performance – Number Range Buffering - 1
When loading large quantities of data into an InfoCube, the number range buffer
should be increased for the dimensions that are likely to have a high number of values
Use Function module RSD_CUBE_GET to Identify the object name of the
dimension.
D t L d P f N b b ff i 2
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Data Load Performance – Number range buffering - 2
Use Transaction SNRO ->EDIT-> Setup Buffering->Main memory
Never set buffering for package dimension
set the values between
500 and 1000.
D t L d P f L d B l i 1
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Data Load Performance – Load Balancing - 1
Use transaction SMLG and create logon group
Logon group for Load balancing
BW system
Data Load Performance Load balancing 2
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Data Load Performance – Load balancing - 2
Change RFC settings in the source system
Use transaction SM59
Source system
Data Load Performance
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Data Load Performance
Drop Unused Partitions
Use program SAP_DROP_EMPTY_FPARTITIONS
Avoid Long execution times for Index rebuild
Data Load Performance
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Data Load Performance
Drop and recreate source twin Structures
Program LMCSWTOP set flag FLG_DCT = ‘X’
Drop and Recreate Bitmap indexes on F table
Use Parallel Processing
ODS loads & activation parallel
Check Update / Transfer rules
Index on source tables for full loads with selection
Define PSA Part. size using transaction RSCUSTV6
Minimum no. of records in a PSA partition Use program SAP_PSA_PARTITION_COMPRESS for existing PSA
tables
Compress Cubes and aggregates regularly
Agenda
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BW Technical Overview
Modeling
Extraction
Changerun
Queries
Compression & Aggregates Rollup
SEM BPS Functions
Agenda
Change run performance - 1
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Change run performance - 1 How Change run works on aggregates
Inserts new records positively and old records negatively
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Change run Performance – Threshold & Block size
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Change run Performance Threshold & Block size
Transaction RSCUSTV8
If the Changes more than 20%
Aggregates are rebuilt
Choose the block size
depending on the Temporary
table space available
Change run Performance - Setting Change run Parallel -1
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Change run Performance Setting Change run Parallel 1
Create an RFC server group using transaction RZ12
Change run Performance - Setting Change run Parallel -2
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Change run Performance Setting Change run Parallel 2
After the RFC server group is defined use report SAP_RSADMIN_MAINTAIN to createthe object entries CR_RFCGROUP in the table RSADMIN
OSS Note – 534630 Parallel Processing of Change run
Parallel Processing of Change run Increases memory consumption
Maximum no. of work
processes
RFC Server Group
Change run Performance – Monitoring - 1
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Change run Performance Monitoring 1
Use Table RSDDSTATAGGR to find the run times
Change run Performance – Monitoring - 2
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g g
Program RSDDS_CHANGERUN_MONITOR
Agenda
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BW Technical Overview
Modeling
Extraction
Changerun
Queries
Compression & Aggregates Rollup
SEM BPS Functions
g
Query Performance - 1
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Q y
Cause of Query Performance issues
Database Database parameters may not be set properly(OSS note :180605)
Database Statistics is not setup properly
Degenerated Indexes
Check if database partitioning is used
Possibility of building an aggregate
ORACLE_PARALLEL_DEGREE value in RSADMIN
OLAP
BW Basis parameters (OSS Note : 192658)
Buffers, I/O, CPU, memory on the database server are exhausted
Check the Read mode of queries / cubes
Check the no. of available dialog processes Usage of restricted / Calculated keyfigures in the query
Frontend
Check the amount of data being transferred
Query Performance - 2
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Build Aggregates
OLAP Cache
Pre Calculated Value Sets
Pre Calculated Web Templates
Logical Partitioning - Multi Providers
Physical Partitioning Look for degenerated Indexes
Compress Infocubes regularly
Query Performance – OLAP Cache
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OLAP Cache Settings using transaction RSCUSTV14
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Query Performance – Pre-Calculated Valuesets
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Query Performance Tuning Techniques - 2
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ST03N Expert mode
Query Performance Tuning Techniques - 3
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Run Transaction SM66
Identify the process id
Run transaction ST04 and find the process in Oracle session
Get the execution Plan
Partition start and Stop
Query Performance Tuning Techniques - 4
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Switch on/off aggregates
Query Performance Tuning Techniques - 5
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Read mode (Table RSRREPDIR)
Tables RSDDSTAT / RSDDSTATAGGR
Cache Monitor(RSRCACHE)
Function ModuleRSDDCVER_RFC_BW_STATISTICS
BW Stats Cubes
RSRT RSRTRACE
Agenda
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BW Technical Overview
Modeling
Extraction
Changerun
Queries
Compression & Aggregates Rollup
SEM BPS Functions
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Aggregate Rollup & Compression Performance
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Aggregates Rollup Compress Cubes & aggregates regularly
Rebuild Indexes on E table of aggregates
Build statistics on Cubes & Aggregates
Include partitioning Characteristic in the aggregates
Do not build stats on the F table of the cube if the table is huge
Compression Performance
Make sure P index exists on the E table of Cube & aggregates
Aggregates F table size
Agenda
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BW Technical Overview
Modeling
Extraction
Changerun
Queries
Compression & Aggregates Rollup
SEM BPS Functions
SEM BPS Performance - 1A i S i i
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Activate Statistics
Transaction BPS_STAT0
Activate Stats
SEM BPS Performance - 2
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Database Parameter Settings ( OSS Note : 358921)
Executing Specialized functions faster than General
functions (eg:- Copy function instead of writing
formula to copy)
Program UPC_BW_SELECTION_DEBUG can be used
to do the analysis of read access in SEM-BPS
Save the results after ending a planning sessionbefore starting another transaction since the memory
is released only after the save or exiting the session.
Compress the Infocubes
Build aggregate if possible
Code Formula functions effectively avoiding multiple
loops
Summary
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Check Oracle Parameter & Basis Parameter
Settings
Deploy Parallel Processing wherever possible
Look for degenerated Indexes
Use transactions ST03N / ST04 / ST02
ROIDOCPRMS table settings for Load Performance
Make use of Load balancing Use OLAP Cache
Use Pre-Calculated Value sets and Web Templates
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